公开(公告)号：US20170008650A1

公开(公告)日：2017-01-12

申请号：US15106690

申请日：2014-09-02

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Tianxu ZHANG ,  Liangliang WANG ,  Gang ZHOU ,  Ming LI ,  Weidong YANG ,  Kuan LIU ,  Yayun ZHENG

IPC: B64G3/00 ,  G06K9/62 ,  G06K9/00 ,  G06K9/46 ,  G01C21/24 ,  G01C21/20

CPC classification number: B64G3/00 ,  B64G1/24 ,  B64G2001/245 ,  G01C21/20 ,  G01C21/24 ,  G06K9/00201 ,  G06K9/4604 ,  G06K9/6202 ,  G06T7/75 ,  G06T2207/10032

Abstract: An attitude estimation method for an on-orbit three-dimensional space object comprises an offline feature library construction step and an online attitude estimation step. The offline feature library construction step comprises: according to a space object three-dimensional model, acquiring multi-viewpoint characteristic views of the object, and extracting geometrical features therefrom to form a geometrical feature library, where the geometrical features comprise an object main body height-width ratio, an object longitudinal symmetry, an object horizontal symmetry, and an object main-axis inclination angle. The online attitude estimation step comprises: preprocessing an on-orbit object image to be tested and extracting features, and matching the extracted features in the geometrical feature library, where an object attitude characterized by a characteristic view corresponding to a matching result is an attitude estimation result. A dimension scale and position relationship between various components of an object are accurately acquired in a three-dimensional modeling stage, thereby ensuring subsequent relatively high matching precision. An attitude estimation system for an on-orbit three-dimensional space object is also provided.

Abstract translation: 轨道三维空间物体的姿态估计方法包括离线特征库构造步骤和在线姿态估计步骤。 离线特征库构建步骤包括：根据空间对象三维模型，获取对象的多视点特征视图，并从中提取几何特征以形成几何特征库，其中几何特征包括对象主体高度 物体纵向对称性，物体水平对称性和物体主轴倾斜角度。 在线姿态估计步骤包括：对要测试的轨道上物体图像进行预处理并提取特征，并且匹配几何特征库中提取的特征，其中以对应于匹配结果的特征视图为特征的对象姿态是姿态估计 结果。 在三维建模阶段中准确地获取对象的各个组件之间的尺度尺度和位置关系，从而确保随后的相对较高的匹配精度。 还提供了一种用于轨道上三维空间物体的姿态估计系统。

公开(公告)号：US20160321788A1

公开(公告)日：2016-11-03

申请号：US15022872

申请日：2015-02-10

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Tianxu ZHANG ,  Gang ZHOU ,  Ao ZHONG ,  Liangliang WANG ,  Ming LI ,  Cen LU ,  Wen ZHANG ,  Zhiyong ZUO

IPC: G06T5/00

CPC classification number: G06T5/003 ,  G06T5/00 ,  G06T2207/20004 ,  G06T2207/20012 ,  G06T2207/20056

Abstract: The invention discloses a direction-adaptive image deblurring method, comprising steps of: (1) defining a minimum cost function for deblurring an image by direction-adaptive total variation regularization; (2) converting the unconstrained minimization problem in step (1) to a constrained problem by auxiliary variables d1=Hu, d2=∇xu and d3=∇yu; (3) obtaining a new minimum cost function from the constrained problem in step (2) by introducing penalty terms; and (4) converting the minimization problem in step (3) to an alternating minimization problem about u, d1, d2 and d3, where a minimum of a variable is calculated as other variables are determined, and obtaining a deblurred image by solving the alternating minimization problem by an alternative and iterative minimization process. Compared with the prior art, the present invention obtains a new direction-adaptive cost function by introducing local direction information into a maximum a posteriori algorithm, solves a problem of edges of an image restored by traditional TV regularization terms being blurred, and can restore images of complex blurring types or images with abundant textures.

Abstract translation: 本发明公开了一种方向自适应图像去模糊方法，包括以下步骤：（1）通过方向自适应总变异规则化定义去除图像的最小成本函数; （2）通过辅助变量d1 = Hu，d2 =∇xu和d3 =∇yu将步骤（1）中的无约束最小化问题转换为约束问题; （3）通过引入惩罚条件，在步骤（2）从约束问题中获得新的最小成本函数; 和（4）将步骤（3）中的最小化问题转换为关于u，d1，d2和d3的交替最小化问题，其中确定变量的最小值作为其他变量，并且通过求解交替 通过替代和迭代最小化过程的最小化问题。 与现有技术相比，本发明通过将局部方向信息引入到最大后验算法中来获得新的方向自适应成本函数，解决了由传统TV正则化项被模糊而恢复的图像的边缘的问题，并且可以恢复图像 复杂的模糊类型或具有丰富纹理的图像。